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Reinforcement Learning for Multi-Period Resource Allocation Tasks

MCML Authors

Abstract

This dissertation develops reinforcement learning methods for resource allocation problems, with a particular focus on financial applications and constrained decision-making. It introduces new approaches for handling linear allocation constraints with guaranteed feasibility and proposes an efficient investment strategy framework that generalizes across different risk preferences, outperforming existing methods on real-world financial data. (Shortened.)

phdthesis Win25a


Dissertation

LMU München. Dec. 2025

Authors

D. Winkel

Links

DOI

Research Area

 A3 | Computational Models

BibTeXKey: Win25a

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